Information doesn't just provide a window on the business, increasingly it is the business. The global economy is moving from products to services which are described almost entirely electronically. Even those businesses that are traditionally associated with making things are less concerned with managing the manufacturing process (which is largely outsourced) than they are with maintaining their intellectual property. Information-Driven Business helps you to understand this change and find the value in your data. Hillard explains techniques that organizations can use and how businesses can apply them immediately. For example, simple changes to the way data is described will let staff support their customers much more quickly; and two simple measures let executives know whether they will be able to use the content of a database before it is even built. This book provides the foundation on which analytical and data rich organizations can be created. Innovative and revealing, this book provides a robust description of Information Management theory and how you can pragmatically apply it to real business problems, with almost instant benefits. Information-Driven Business comprehensively tackles the challenge of managing information, starting with why information has become important and how it is encoded, through to how to measure its use.
Preface. Acknowledgments. Chapter 1: Understanding the Information Economy. Did the Internet Create the Information Economy? Origins of Electronic Data Storage. Stocks and Flows. Business Data. Changing Business Models. Information Sharing versus Infrastructure Sharing. Governing the New Business. Success in the Information Economy. Note. Chapter 2: The Language of Information. Structured Query Language. Statistics. XQuery Language. Spreadsheets. Documents and Web Pages. Knowledge, Communications, and Information Theory. Notes. Chapter 3: Information Governance. Information Currency. Economic Value of Data. Goals of Information Governance. Organizational Models. Ownership of Information. Strategic Value Models. Repackaging of Information. Lifecycle. Notes. Chapter 4: Describing Structured Data. Networks and Graphs. Brief Introduction to Graphs. Relational Modeling. Relational Concepts. Cardinality and Entity-Relationship Diagrams. Normalization. Impact of Time and Date on Relational Models. Applying Graph Theory to Data Models. Directed Graphs. Normalized Models. Note. Chapter 5: Small Worlds Business Measure of Data. Small Worlds. Measuring the Problem and Solution. Abstracting Information as a Graph. Metrics. Interpreting the Results. Navigating the Information Graph. Information Relationships Quickly Get Complex. Using the Technique. Note. Chapter 6: Measuring the Quantity of Information. Definition of Information. Thermal Entropy. Information Entropy. Entropy versus Storage. Decision Entropy. Conclusion and Application. Notes. Chapter 7: Describing the Enterprise. Size of the Undertaking. Enterprise Data Models Are All or Nothing. The Data Model as a Panacea. Metadata. The Metadata Solution. Master Data versus Metadata. The Metadata Model. XML Taxonomies. Metadata Standards. Collaborative Metadata. Metadata Technology. Data Quality Metadata. History. Executive Buy-in. Notes. Chapter 8: A Model for Computing Based on Information Search. Function-Centric Applications. An Information-Centric Business. Enterprise Search. Security. Metadata Search Repository. Building the Extracts. The Result. Note. Chapter 9: Complexity, Chaos, and System Dynamics. Early Information Management. Simple Spreadsheets. Complexity. Chaos Theory. Why Information Is Complex. Extending a Prototype. System Dynamics. Data as an Algorithm. Virtual Models and Integration. Chaos or Complexity. Notes. Chapter 10: Comparing Data Warehouse Architectures. Data Warehousing. Contrasting the Inmon and Kimball Approaches to Data Warehouses. Quantity Implications. Usability Implications. Historical Data. Summary. Notes. Chapter 11: Layered View of Information. Information Layers. Are They Real? Turning the Layers into an Architecture. The User Interface. Selling the Architecture. Chapter 12: Master Data Management. Publish and Subscribe. About Time. Granularity, Terminology, and Hierarchies. Rule #1: Consistent Terminology. Rule #2: Everyone Owns the Hierarchies. Rule #3: Consistent Granularity. Reconciling Inconsistencies. Slowly Changing Dimensions. Customer Data Integration. Extending the Metadata Model. Technology. Chapter 13: Information and Data Quality. Spreadsheets. Referencing. Fit for Purpose. Measuring Structured Data Quality. A Scorecard. Metadata Quality. Extended Metadata Model. Notes. Chapter 14: Security. Cryptography. Public Key Cryptography. Applying PKI. Predicting the Unpredictable. Protecting an Individual s Right to Privacy. Securing the Content versus Securing the Reference. Chapter 15: Opening up to the Crowd. A Taxonomy for the Future. Populating the Stakeholder Attributes. Reducing Email Traffic within Projects. Managing Customer Email. General Email. Preparing for the Unknown. Charters. Information Is Dynamic. Power of the Crowd Can Improve your Data Quality. Note. Chapter 16: Building Incremental Knowledge. Bayesian Probabilities. Information from Processes. The MIT Beer Game. Hypothesis Testing and Confidence Levels. Business Activity Monitoring. Note. Chapter 17: Enterprise Information Architecture. Website Information Architecture. Extending the Information Architecture. Business Context. Users. Content. Top-Down/Bottom-Up. Presentation Format. Project Resourcing. Information to Support Decision Making. Note. Looking to the Future. About the Author. Index.
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